论文标题
资源感知的分布式supdodular最大化：多机器人决策的范式
Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
论文作者
论文摘要
多机器人决策是多个机器人协调操作的过程。在本文中，尽管机器人有限的车载资源以及其任务的资源规定复杂性，但我们的目标是高效有效的多机器人决策。我们介绍了第一种算法，使机器人可以选择其他几个机器人来协调和证明是平衡集中式和分散协调的权衡。特别是，集中化有利于全球近乎最佳的决策，但付费增加了船上资源要求；而权力下放有利于最小的资源要求，但以全球次优的成本。因此，所有机器人都可以负担我们的算法，无论其资源如何。我们受到自治的未来的激励，涉及多个机器人协调行动以完成资源需求任务，例如目标跟踪，区域覆盖范围和监视。为了提供封闭形式的保证，我们专注于涉及单调和“双重”下函数的最大化问题。为了捕获权力下放的成本，我们介绍了在非邻居（Coin）中的信息集中概念。我们在图像覆盖的模拟场景中验证算法。
Multi-robot decision-making is the process where multiple robots coordinate actions. In this paper, we aim for efficient and effective multi-robot decision-making despite the robots' limited on-board resources and the often resource-demanding complexity of their tasks. We introduce the first algorithm enabling the robots to choose with which few other robots to coordinate and provably balance the trade-off of centralized vs. decentralized coordination. Particularly, centralization favors globally near-optimal decision-making but at the cost of increased on-board resource requirements; whereas, decentralization favors minimal resource requirements but at a global suboptimality cost. All robots can thus afford our algorithm, irrespective of their resources. We are motivated by the future of autonomy that involves multiple robots coordinating actions to complete resource-demanding tasks, such as target tracking, area coverage, and monitoring. To provide closed-form guarantees, we focus on maximization problems involving monotone and "doubly" submodular functions. To capture the cost of decentralization, we introduce the notion of Centralization Of Information among non-Neighbors (COIN). We validate our algorithm in simulated scenarios of image covering.
